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About This Role
Who We Are
BH Management, LLC is a people\-first multifamily owner and operator that grew from a small startup into one of the nation's largest commercial real estate companies. Founded in 1993, BH is celebrated for its simple commitment to doing business the right way and investing in its team. Today, BH manages over 100,000 units, employs over 2,800 people, owns its processes in\-house, and is praised by Fortune Magazine as the “Best Workplace for Women,” “Best Workplace for Millennials,” and “Best Workplaces for Diversity.” Powered by innovation and a can\-do attitude, BH improves daily, striving to construct a smarter way to live, invest, manage, and grow.
BH is passionate about setting the standard in the multifamily industry. We are a welcoming band of go\-getters who think big, sweat the details, and take our work (but never ourselves) too seriously. We set our sights high, own our mistakes, and turn lemons into lemonade. We are incredibly proud of where we’ve come and are ready to tackle what’s next. Come join us!
Position Summary:
The Analytics \& AI Engineer designs, builds, and optimizes the data platforms, pipelines, and intelligent solutions that power reporting, advanced analytics, and AI\-driven decision making across the organization. This role is responsible for the end\-to\-end engineering of analytics capabilities, from data ingestion and transformation to scalable architecture, automation, and AI integration. This role takes a builder and problem\-solver who thinks in systems, pipelines, and reusable frameworks. They leverage modern data engineering practices to create reliable, scalable solutions while thoughtfully applying artificial intelligence and machine learning technologies to drive measurable business outcomes. This role serves as a key bridge between data, technology, and business stakeholders, enabling both current analytical needs and future AI innovation.
Essential Job Functions:
- Design, build, and maintain scalable data pipelines (ETL/ELT) that integrate operational, financial, and business data from multiple source systems.
- Architect, develop, and optimize data models, warehouses, and semantic layers that support enterprise reporting, self\-service analytics, and Power BI/SQL\-based solutions.
- Develop and maintain Python\-based data workflows, automation processes, data quality frameworks, and monitoring systems that ensure reliability and performance.
- Identify, evaluate, and implement AI and machine learning solutions, including forecasting, anomaly detection, predictive analytics, and generative AI capabilities, where they create measurable business value.
- Build and support AI\-enabled workflows, including large language model (LLM) integrations, intelligent document processing, data extraction, summarization, and conversational analytics capabilities.
- Establish and promote best practices for analytics and AI engineering, including documentation, testing, version control, data governance, CI/CD, and model lifecycle management.
- Partner with Data Science, Software Development, and business leaders to design scalable data architectures that support analytics, AI, and machine learning initiatives.
- Lead automation efforts that reduce manual processes, improve data quality, and increase operational efficiency across the organization.
- Evaluate emerging analytics and AI technologies, recommending solutions that improve business performance, scalability, and user experience.
- Serve as a technical subject matter expert for data infrastructure, analytics platforms, AI solutions, and the integration of new technologies and data sources.
- Collaborate with IT, Security, and business stakeholders to ensure data and AI solutions are secure, governed, compliant, and aligned with organizational standards.
- Support the development of data products and intelligent applications that enable business teams to make faster, more informed decisions.
- Other duties as assigned.
Minimum Qualifications/Skills:
- 3\-5 years of experience in analytics engineering, data engineering, AI engineering, software engineering, or a related technical discipline; experience in multifamily, real estate, financial services, or similar industries is preferred.
- Strong proficiency in SQL and Python, with demonstrated experience building and maintaining scalable data pipelines, ETL/ELT processes, and analytical solutions.
- Experience designing data models, warehouses, and reporting architectures that support business intelligence and advanced analytics.
- Hands\-on experience with cloud data platforms and services such as Azure, Snowflake, AWS, Databricks, or similar technologies.
- Familiarity with AI and machine learning tools, frameworks, and APIs, including predictive modeling, forecasting, LLMs, generative AI platforms, and automation technologies.
- Understanding of software engineering principles, including version control, testing, documentation, DevOps, and CI/CD practices.
- Strong analytical and problem\-solving skills with the ability to independently design, implement, and optimize technical solutions.
- Ability to translate business requirements into scalable technical architecture and actionable insights.
- Strong communication and collaboration skills, with the ability to explain technical concepts to both technical and non\-technical audiences.
- Curiosity, adaptability, and a passion for leveraging data and AI to solve complex business challenges and create measurable impact
Work Schedule: Monday\-Friday (work schedule may vary depending on business needs).
BH is an Equal Employment Opportunity Employer. We foster the diverse voices of our community by advocating for inclusivity, celebrating our differences, and continually evolving our practice to make BH a better place to work and live. Our posted compensation reflects the cost of talent across multiple US geographic markets. Pay is based on a number of factors and may vary depending on job\-related knowledge, skills, and experience.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At BH Management Services, LLC, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
BH Management Services, LLC AI Hiring
BH Management Services, LLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in IA, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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